def test_weibull(rnd, a, loc=0, scale=1):
# weibull random variate generator
weibull_dist = RVGs.Weibull(a=a, scale=scale, loc=loc)
# obtain samples
samples = get_samples(weibull_dist, rnd)
# get theoretical variance
var = scipy.weibull_min.stats(a, loc, scale, moments='v')
var = np.asarray(var).item()
# report mean and variance
print_test_results('Weibull',
samples,
expectation=math.gamma(1.0 + 1 / a) * scale + loc,
variance=var)
def test_fitting_weibull():
print("\nTesting Weibull with a=5, scale=2, loc=1")
dist = RVGs.Weibull(a=5, scale=2, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.Weibull.fit_mm(mean=np.average(data),
st_dev=np.std(data),
fixed_location=1)
dict_ml_results = RVGs.Weibull.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_weibull_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_weibull_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
